Restoring SAR images using Effective Image Restoration Approach

Landsat 7 Enhanced Thematic Mapper Plus satellite images presents an important data source for many applications related to remote sensing. An effective image restoration method is proposed to fill the missing information in the satellite images. The segmentation of satellite images to find the SLIC Super pixels and then to find the image Segments. The Boundary Reconstruction is performed using Edge Matching to find the area of the missing region. Peak Signal to Noise Ratio and Root Mean Square Error using with boundary reconstruction and without boundary reconstruction to evaluate the quality and the error rate of the satellite images. The results show the capability to predict the missing values accurately in terms of quality, time without need of external information.The values for PSNR has changed from 25 to 90 and RMSE has changed from 180 to 4 in Red Channel of an image.This indicates that quality of the image is high and error rate is less.

CNN-Enhanced Multi-Indices Patch-Based Classification: A Case Study of Guwahati City

Land use and land cover (LULC) provides a way to classify objects on the surface of Earth. This paper aims to identify the varying land cover classes by stacking of 6 spectral bands and 10 different generated indices from those bands together. We have considered the multispectral images of Landsat 7 for our research. It is seen that instead of using only basic spectral bands (blue, green, red, nir, swir1 and swir2) for classification, stacking relevant indices of multiple target classes like ndvi, evi, nbr, BU, etc. with basic bands generates more precise results. In this study, we have used automated clustering techniques for generating 5 different class labels for training the model. These labels are further used to develop a predictive model to classify LULC classes. The proposed classifier is compared with the SVM and KNN classifiers. The results show that this proposed strategy gives preferable outcomes over other techniques. After training the model over 50 epochs, an accuracy of 93.29% is achieved. Keywords: Land use, land cover, CNN, ISODATA, indices

Land use and land cover changes in the Lower Neretva Region from 1990 to 2020

Changes in land use and land cover are the result of complex interactions between humans and their environment. This study examines land use and land cover changes in the Lower Neretva Region between 1990 and 2020. Political and economic changes in the early 1990s resulted in changes in the landscape, both directly and indirectly. Multispectral image processing was used to create thematic maps of land use and land cover for 1990, 2005, and 2020. Satellite images from Landsat 5, Landsat 7 and Landsat 8 were the main source of data. Land use and land cover structure was assessed using a hybrid approach, combining unsupervised and manual (visual) classification methods. An assessment of classification accuracy was carried out using a confusion matrix and kappa coefficient. According to the results of the study, the percentage of built-up areas increased by almost 33%. Agricultural land and forests and grasslands also increased, while the proportion of swamps and sparse vegetation areas decreased.

Analysis of Land Use and Land Cover Changes in Urban Areas Using Remote Sensing: Case of Blantyre City

Blantyre City has experienced a wide range of changes in land use and land cover (LULC). This study used Remote Sensing (RS) to detect and quantify LULC changes that occurred in the city throughout a twenty-year study period, using Landsat 7 Enhanced Thematic Mapper (ETM+) images from 1999 and 2010 and Landsat 8 Operational Land Imager (OLI) images from 2019. A supervised classification method using an Artificial Neural Network (ANN) was used to classify and map LULC types. The kappa coefficient and the overall accuracy were used to ascertain the classification accuracy. Using the classified images, a postclassification comparison approach was used to detect LULC changes between 1999 and 2019. The study revealed that built-up land and agricultural land increased in their respective areas by 28.54 km2 (194.81%) and 35.80 km2 (27.16%) with corresponding annual change rates of 1.43 km·year−1 and 1.79 km·year−1. The area of bare land, forest land, herbaceous land, and waterbody, respectively, decreased by 0.05%, 90.52%, 71.67%, and 6.90%. The LULC changes in the study area were attributed to urbanization, population growth, social-economic growth, and climate change. The findings of this study provide information on the changes in LULC and driving factors, which Blantyre City authorities can utilize to develop sustainable development plans.

Uzaktan Algılama Yöntemleri Uygulanarak Namak Gölü'nün Su Yüzeyindeki Değişimin Su İndeksleri (AWEI, MNDWI, NDWI VE WRI) Aracılığıyla Belirlenmesi

Namak Gölü, İran’da bulunan Urmiye Gölü, Hazar Denizi ve diğer su kütlelerini oluşturan Paratetis denizinin bir kalıntısıdır. Göl, küçük bir tuz gölü olmanın yanı sıra deniz seviyesinden 790 metre yükseklikte yer almakta ve Kum (Qom) nehri tarafından beslenmektedir. Bununla birlikte, son yıllarda kuraklığın etkisiyle, azalan yüzey suyu ve artan tuzluluk oranı gölün kurumaya yüz tutmasına neden olmuştur. Bu çalışmada, 2001-2021 yılları arasında -belirlenen onar yıllık üç dönemde- Namak Gölü'nün mekânsal-zamansal değişimleri; Landsat 5-TM, Landsat 7-ETM+ ve Landsat 8-OLI görüntüleri kullanılarak hesaplanmıştır. Çalışmada, yüzey suyunun belirlenmesini sağlayan Normalleştirilmiş Fark Su İndeksi (NDWI), Modifiye Edilmiş Fark Su İndeksi (MNDWI), Su Oranı İndeksi (WRI) ve Landsat verilerinden yüzey suyunun çıkarılmasına imkân veren Otomatik Su Çıkarma İndeksi (AWEI) incelenmiştir. Sonuç olarak, 20 yıllık dönemde meydana gelen su yüzeyindeki değişiklikler alansal olarak (km²) karşılaştırılmış ve doğruluk oranı görece yüksek olan NDWI indeksinin, diğer indekslere göre yüzey suyunun belirlenmesinde daha faydalı bir yöntem olarak kullanılabileceği belirlenmiştir.

Land Cover Changes Based on Cellular Automata for Land Surface Temperature in Semarang Regency

Land cover changes based on cellular automata for surface temperature in Semarang Regency has increased significantly due to the continuous rise in its population. Therefore, this study aims to identify, analyze and predict multitemporal land cover changes and surface temperature distribution in 2028. Data on the land cover map were obtained from Landsat 7 and 8 based on supervised classification, while Land Surface Temperature (LST) was calculated from its thermal bands. The collected data were analyzed for accuracy through observation, while Cellular Automata - Markov Chain was used to predict the associated changes in 2028. The result showed that there are 4 land cover maps with 5-year intervals from 2003 to 2018 at an accuracy of more than 85%. Furthermore, the existing land covers were dominated by forest with decreasing trend, while the built-up area continuously increased. The existing Land surface temperature range from 20.6°C to 36.6°C, at an average of 28.2°C and a yearly increase of 0.07°C. The temperature changes are positively correlated with the occurrence of land conversion. Land cover predictions for 2028 show similar forest dominance, with a 23,4% built-up area at a surface temperature of 28.9°C. Keywords: Land cover change; Cellular Automata-Markov Chain; Land Surface Temperature Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Multitemporal Analysis as a Non-Invasive Technology Indicates a Rapid Change in Land Use in the Amazon: The Case of the ITT Oil Block

The Amazon Region of Ecuador (ARE) hosts a great variety of biodiversity and ecosystems. These hotspots are internationally recognized for presenting unique fauna and flora found nowhere else in the world. Within the ARE, there is the Yasuní National Park (YNP), a recognized Biosphere Reserve located in the sub-basins of various rivers. The study area is the “ITT Oil Block” (Ishpingo, Tambococha, and Tiputini), situated in the Province of Orellana and superimposed on the YNP. The block has an area of 179,449.53 ha. The main objective of the current study was to analyze the multi-temporality of land-use change in the ITT Oil Block of the ARE. In the methodological process, the PCI Geomatic and ARCGIS programs were used for the processing and classification of satellite images (Landsat 7 and 8). The changes in land use in the ITT Oil Block over the three periods (2001, 2014, and 2017) indicated that forest cover decreased by 24.23% in soils, while infrastructure and cultivation increased throughout the time period by 0.27% and 0.23%, respectively. The most significant land-use change rate in the ITT Oil Block in the period 2001–2017 are the categories of bare soil with 9.01% (10,640.82 ha) and cultivation with 7.27% (591.29 ha).

THE RELATIONSHIP OF LAND USE CHANGES AND LAND SURFACE TEMPERATURE FOR URBAN AREA IN KUCHING, SARAWAK

Land surface temperature (LST) is used as an indicator for land temperature.Previous research demonstrates a strong correlation between urban growth andland surface temperature. The rising of land temperature will lead to urban heatisland if there are no preventative precautions done. Due to the area's rapidurbanisation, this study will focus on Kuching City. Matang Jaya, Tabuan Jaya,Satok, and Batu Kawa were chosen as case studies. These areas are rapidlydeveloping, with new townships and population growth. The Landsat 7 data setwas used as secondary data in this study. Spatial and thermal analysis wereperformed on the output using ERDAS software and ArcGIS. The analysesderived land use changes between 2005 and 2017, temperature statistics for landuse types, and LST retrieval for case studies. The result indicates that the landsurface temperature increased with the case studies' physical development.

Harmonizing Surface Reflectance Between Landsat-7 ETM+, Landsat-8 OLI, and Sentinel-2 MSI

Remote sensing dynamic monitoring methods often benefit from a dense time series of observations. To enhance these time series, it is sometimes necessary to integrate data from multiple satellite systems. For more than 40 years, Landsat has provided the longest time record of space-based land surface observations, and the successful launch of the Landsat-8 Operational Land Imager (OLI) sensor in 2013 continues this tradition. However, the 16-day observation period of Landsat images has challenged the ability to measure subtle and transient changes like never before. The European Space Agency (ESA) launched the Sentinel-2A satellite in 2015. The satellite carries a Multispectral Instrument (MSI) sensor that provides a 10-20m spatial resolution data source providing an opportunity to complement the Landsat data record. The collection of Sentinel-2A MSI, Landsat-7 ETM+, and Landsat-8 OLI data provide multispectral global coverage from 10m to 30m with further reduced data revisit intervals. There are many differences between sensor data that need to be taken into account to use these data together reliably. The purpose of this study is to evaluate the potential of integrating surface reflectance data from Landsat-7, Landsat-8 and Sentinel-2 archived in the Google Earth Engine (GEE) cloud platform. To test and quantify the differences between these sensors, hundreds of thousands of surface reflectance data from sensor pairs were collected over China. In this study, some differences in the surface reflectance of the sensor pairs were identified, based upon which a cross-sensor conversion model was proposed, i.e., a suitable adjustment equation was fitted using an ordinary least squares (OLS) linear regression method to convert the Sentinel-2 reflectance values closer to the Landsat-7 or Landsat-8 values. The regression results show that the Sentinel MSI data are spectrally comparable to both types of Landsat image data, just as the Landsat sensors are comparable to each other. The root mean square error (RMSE) values between MSI and Landsat spectral values before coordinating the sensors ranged from 0.014 to 0.037, and the RMSE values between OLI and ETM + ranged from 0.019 to 0.039. After coordination, RMSE values between MSI and Landsat spectral values ranged from 0.011 to 0.026, and RMSD values between OLI and ETM + ranged from 0.013 to 0.034. The fitted adjustment equations were also compared to the HLS (Harmonized Landsat-8 Sentinel-2) global fitted equations (Sentinel-2 to Landsat-8) published by the National Aeronautics and Space Administration (NASA) and were found to be significantly different, increasing the likelihood that such adjustments would need to be fitted on a regional basis. This study believes that despite the differences in these datasets, it appears feasible to integrate these datasets by applying a linear regression correction between the bands.